{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5a27a4c-830e-4f0a-83c3-bf3ad4c16e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "import pandas as pd\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4e625a6-5ecd-49dc-8cbd-a8529fde7ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load file\n",
    "df = pd.read_csv('cortisol_measured.csv')\n",
    "df = df.dropna()\n",
    "df.Treatment.unique()\n",
    "df = df[(df.Treatment=='control ')| (df.Treatment=='light pulse ')|(df.Treatment=='continous')| \n",
    "       (df.Treatment=='control2')|(df.Treatment=='voltage5V_15min')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cee88147-9840-4700-b007-f6dfc0eed8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "control mean: log Con                0.021525\n",
      "concentration ng/mL    1.089202\n",
      "dtype: float64 control std: log Con                0.124383\n",
      "concentration ng/mL    0.322890\n",
      "dtype: float64\n",
      "continous mean: log Con                0.209450\n",
      "concentration ng/mL    1.783645\n",
      "dtype: float64 continous std: log Con                0.210018\n",
      "concentration ng/mL    0.902677\n",
      "dtype: float64\n",
      "light pulse mean: log Con                0.191244\n",
      "concentration ng/mL    1.641810\n",
      "dtype: float64 light pulse std: log Con                0.152258\n",
      "concentration ng/mL    0.632008\n",
      "dtype: float64\n",
      "control 2 mean: log Con               -0.100718\n",
      "concentration ng/mL    0.868888\n",
      "dtype: float64 control 2 std: log Con                0.266570\n",
      "concentration ng/mL    0.502181\n",
      "dtype: float64\n",
      "voltage mean: log Con                0.601653\n",
      "concentration ng/mL    4.032844\n",
      "dtype: float64 voltage std: log Con                0.083049\n",
      "concentration ng/mL    0.766527\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-96-04c4d48315f3>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(f\"control mean: {df[(df.Treatment=='control ')].mean()} control std: {df[(df.Treatment=='control ')].std()}\")\n",
      "<ipython-input-96-04c4d48315f3>:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(f\"continous mean: {df[(df.Treatment=='continous')].mean()} continous std: {df[(df.Treatment=='continous')].std()}\")\n",
      "<ipython-input-96-04c4d48315f3>:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(f\"light pulse mean: {df[(df.Treatment=='light pulse ')].mean()} light pulse std: {df[(df.Treatment=='light pulse ')].std()}\")\n",
      "<ipython-input-96-04c4d48315f3>:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(f\"control 2 mean: {df[(df.Treatment=='control2')].mean()} control 2 std: {df[(df.Treatment=='control2')].std()}\")\n",
      "<ipython-input-96-04c4d48315f3>:5: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  print(f\"voltage mean: {df[(df.Treatment=='voltage5V_15min')].mean()} voltage std: {df[(df.Treatment=='voltage5V_15min')].std()}\")\n"
     ]
    }
   ],
   "source": [
    "print(f\"control mean: {df[(df.Treatment=='control ')].mean()} control std: {df[(df.Treatment=='control ')].std()}\")\n",
    "print(f\"continous mean: {df[(df.Treatment=='continous')].mean()} continous std: {df[(df.Treatment=='continous')].std()}\")\n",
    "print(f\"light pulse mean: {df[(df.Treatment=='light pulse ')].mean()} light pulse std: {df[(df.Treatment=='light pulse ')].std()}\")\n",
    "print(f\"control 2 mean: {df[(df.Treatment=='control2')].mean()} control 2 std: {df[(df.Treatment=='control2')].std()}\")\n",
    "print(f\"voltage mean: {df[(df.Treatment=='voltage5V_15min')].mean()} voltage std: {df[(df.Treatment=='voltage5V_15min')].std()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "28bfd544-83ca-42d5-ac80-f4dcaeafec69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Treatment', y='concentration ng/mL', data=df, dodge=True, color=\"grey\")\n",
    "ax = sns.swarmplot(x='Treatment', y='concentration ng/mL', \n",
    "                          data=df, dodge=True, color=\"black\")\n",
    "\n",
    "sns.despine()\n",
    "ax.set(xlabel=\"\", ylabel = \"Cortisol concentration ng/ml\")\n",
    "plt.savefig(r'C:\\Users\\Clava\\OneDrive - Harvard University\\Paper\\OMR sleep\\Cortisol slaiva measure\\Box_cortisol.pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "21f08afa-12a5-45f9-8bb5-0433edf13547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.0600752489320007, pvalue=0.030881322882370808)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define samples\n",
    "group1 = df[df['Treatment']=='control ']\n",
    "group2 = df[df['Treatment']=='light pulse ']\n",
    "\n",
    "#perform independent two sample t-test\n",
    "ttest_ind(group1['concentration ng/mL'], group2['concentration ng/mL'],alternative ='less')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "413131b5-fd4d-4683-b94f-3aa8dd68a820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-1.9027980770002286, pvalue=0.04311180465997184)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1 = df[df['Treatment']=='control ']\n",
    "group2 = df[df['Treatment']=='continous']\n",
    "\n",
    "#perform independent two sample t-test\n",
    "ttest_ind(group1['concentration ng/mL'], group2['concentration ng/mL'], alternative ='less')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5e075cd2-30aa-4405-be97-a42ac61c9648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-4.882817758634417, pvalue=0.019738004404802483)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1 = df[df['Treatment']=='control2']\n",
    "group2 = df[df['Treatment']=='voltage5V_15min']\n",
    "\n",
    "#perform independent two sample t-test\n",
    "ttest_ind(group1['concentration ng/mL'], group2['concentration ng/mL'],alternative ='less')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
